Document Type : Article

**Authors**

^{1}
Computer Sciences Application and Research Center, Usak University, Usak, Turkey.

^{2}
Department of Computer Engineering, Konya Food and Agriculture University, Konya, Turkey.

**Abstract**

Time series prediction is a remarkable research interest, which is widely followed by scientists / researchers. Because many fields include analyzing processes over such time series, different kinds of approaches, methods, and techniques are often employed in order to achieve alternative prediction ways. It seems that Artificial Intelligence oriented solutions have strong potential on providing effective and accurate prediction approaches in even most complicated time series structures. In the sense of the explanations, this study aims to introduce an alternative, Artificial Intelligence based approach of Artificial Neural Networks, and Cognitive Development Optimization Algorithm, a recent intelligent optimization technique introduced by the authors. Here, it has been aimed to predict different kinds of time series, by using the introduced system / approach. In this way it has been possible to discuss about application potential of the hybrid system and report findings related to its success on prediction. The authors believe that the study has been a good chance to support the literature with an alternative solution approach and see potential of a newly developed, Artificial Intelligence oriented optimization algorithm on different applications.

**Keywords**

- time series prediction
- time series analysis
- Artificial Neural Networks
- cognitive development optimization algorithm (CoDOA)
- Artificial intelligence

**Main Subjects**

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20. Zhao, L. and Yang, Y. "PSO-based single multiplicative neuron model for time series prediction", Expert Systems with Apps, 36, pp. 2805-2812 (2009).

21. Weng, S.S. and Liu, Y.H. "Mining time series data for segmentation by using ant colony optimization", European Journal of Operational Research, 173, pp. 921-937 (2006).

22. Toskari, M.D. "Estimating the net electricity energy generation and demand using the ant colony optimization approach", Energy Policy, 37, pp. 1181- 1187 (2009).

23. Hong, W.C. "Application of chaotic ant swarm optimization in electric load forecasting", Energy Policy, 38, pp. 5830-5839 (2010).

24. Niu, D., Wang, Y., and Wu, D.D. "Power load forecasting using support vector machine and ant colony optimization", Expert Systems with App., 37, pp. 2531-2539 (2010).

25. Yeh, W.-C. "New parameter-free simplified swarm optimization for artificial neural network training and its application in the prediction of time series", IEEE Trans. on Neural Networks and Learning Systems, 24, pp. 661-665 (2013).

26. Nourani, V. and Andalib, G. "Wavelet based artificial intelligence approaches for prediction of hydrological time series", Australasian Conference on Artificial Life and Comp. Intelligence, pp. 422-435, Newcastle, NSW, Australia (2015).

27. Bontempi, G., Taieb, S.B., and Le Borgne, Y.-A. "Machine learning strategies for time series forecasting", In Business Intelligence (Lecture Notes in Business Information Processing), -138, M.-A. Aufaure and E. Zimanyi, Eds., Springer-Verlag (2013).

28. Hu, Y.X. and Zhang, H.T. "Prediction of the chaotic time series based on chaotic simulated annealing and support vector machine", Int. Conference on Solid State Devices and Materials Science, pp. 506-512. Macao, China (2012).

29. Liu, P. and Yao, J.A. "Application of least square support vector machine based on particle swarm optimization to chaotic time series prediction", IEEE Int. Conference on Intelligent Computing and Intelligent Systems, pp. 458-462. Shanghai, China (2009).

30. Quian, J.S., Cheng, J., and Guo, Y.N. "A novel multiple support vector machines architecture for chaotic time series prediction", Advances in Natural Computation, Lecture Notes in C.S., 4221, pp. 147- 156 (2006).

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32. Zhang, J.S., Dang, J.L., and Li, H.C. "Local support vector machine prediction of spatiotemporal chaotic time series", Acta Physica Sinica, 56, pp. 67-77 (2007).

33. Farooq, T., Guergachi, A., and Krishnan, S. "Chaotic time series prediction using knowledge based Green's kernel and least-squares support vector machines", IEEE Int. Conference on Systems, Man and Cybernetics,pp. 2669-2674, Montreal, Cook Islands (2007).

34. Shi, Z.W. and Han, M. "Support vector echo-state machine for chaotic time-series prediction", IEEE Trans. on Neural Networks, 18, pp. 359-372 (2007).

35. Li, H.T. and Zhang, X.F. "Precipitation time series predicting of the chaotic characters using support vector machines", Int. Conference on Information Management, Innovation Management and Indus. Eng., pp. 407-410, Xian, China (2009).

36. Zhu, C.H., Li, L.L., Li, J.H., and Gao, J.S. "Shortterm wind speed forecasting by using chaotic theory and SVM", Applied Mechanics and Materials, 300- 301, pp. 842-847 (2013).

37. Ren, C.-X., Wang, C.-B., Yin, C.-C., Chen, M., and Shan, X. "The prediction of short-term traffic flow based on the niche genetic algorithm and BP neural network", 2012 Int. Conference on Information Technology and Software Engineering, pp. 775-781, Beijing, China (2013).

38. Ding, C., Wang, W., Wang, X., and Baumann, M. "A neural network model for driver's lane-changing trajectory prediction in urban traffic flow", Mathematical Problems in Engineering (Online) (2013). DOI: 10.1155/2013/967358.

39. Yin, H., Wong, S.C., Xu, J., and Wong, C.K. "Urban traffic flow prediction using a fuzzy-neural approach", Transportation Research Part-C: Emerging Technologies, 10, pp. 85-98 (2002).

40. Dunne, S. and Ghosh, B. "Weather adaptive traffic prediction using neurowavelet models", IEEE Trans. on Intelligent Transportation Systems, 14, pp. 370- 379 (2013).

41. Pulido, M., Melin, P., and Castillo, O. "Particle swarm optimization of ensemble neural networks with fuzzy aggregation for time series prediction of the Mexican stock exchange", Information Sciences, 280, pp. 188-204 (2014).

42. Huang, D.Z., Gong, R.X., and Gong, S. "Prediction of wind power by chaos and BP artificial neural networks approach based on genetic algorithm", Journal of Electrical Eng. and Tech., 10(1), pp. 41-46, (2015).

43. Jiang, P., Qin, S., Wu, J., and Sun, B. "Time series analysis and forecasting for wind speeds using support vector regression coupled with artificial intelligent algorithms", Mathematical Prob. in Eng., Article ID 939305 (2015).

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